lets_plot.geom_density

lets_plot.geom_density(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, kernel=None, adjust=None, bw=None, n=None, fs_max=None, **other_args)

Displays kernel density estimate, which is a smoothed version of the histogram.

Parameters

• mapping (FeatureSpec) – Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

• data (dict or DataFrame) – The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

• stat (str, default=’density’) – The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘count’ (counts number of points with same x-axis coordinate), ‘bin’ (counts number of points with x-axis coordinate in the same bin), ‘smooth’ (performs smoothing - linear default), ‘density’ (computes and draws kernel density estimate).

• position (str or FeatureSpec) – Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.

• show_legend (bool, default=True) – False - do not show legend for this layer.

• sampling (FeatureSpec) – Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.

• tooltips (layer_tooltips) – Result of the call to the layer_tooltips() function. Specifies appearance, style and content.

• kernel (str, default=’gaussian’) – The kernel we use to calculate the density function. Choose among ‘gaussian’, ‘cosine’, ‘optcosine’, ‘rectangular’ (or ‘uniform’), ‘triangular’, ‘biweight’ (or ‘quartic’), ‘epanechikov’ (or ‘parabolic’).

• bw (str or float) – The method (or exact value) of bandwidth. Either a string (choose among ‘nrd0’ and ‘nrd’), or a float.

• adjust (float) – Adjust the value of bandwidth my multiplying it. Changes how smooth the frequency curve is.

• n (int, default=512) – The number of sampled points for plotting the function.

• fs_max (int, default=500) – Maximum size of data to use density computation with ‘full scan’. For bigger data, less accurate but more efficient density computation is applied.

• other_args – Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.

Returns

Geom object specification.

Return type

LayerSpec

Note

Computed variables:

• ..density.. : density estimate (mapped by default).

• ..count.. : density * number of points.

• ..scaled.. : density estimate, scaled to maximum of 1.

geom_density() understands the following aesthetics mappings:

• x : x-axis coordinates.

• alpha : transparency level of a layer. Understands numbers between 0 and 1.

• color (colour) : color of a geometry lines. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.

• fill : color of geometry filling.

• size : lines width. Defines line width.

• linetype : type of the line. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’.

• weight : used by ‘density’ stat to compute weighted density.

Examples

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
np.random.seed(42)
x = np.random.normal(size=1000)
ggplot({'x': x}, aes(x='x')) + geom_density()

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 300
np.random.seed(42)
x = np.random.normal(size=n)
c = np.random.choice(['a', 'b', 'c'], size=n)
ggplot({'x': x, 'c': c}, aes(x='x')) + \
    geom_density(aes(group='c', color='c', fill='c'), alpha=.2, \
                 tooltips=layer_tooltips().format('..density..', '.3f')\
                                          .line('density|@..density..')\
                                          .format('..count..', '.1f')\
                                          .line('count|@..count..')\
                                          .format('..scaled..', '.2f')\
                                          .line('scaled|@..scaled..'))

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
np.random.seed(42)
x = np.random.normal(size=1000)
p = ggplot({'x': x}, aes(x='x'))
bunch = GGBunch()
for i, bw in enumerate([.1, .2, .4]):
    for j, n in enumerate([16, 64, 256]):
        bunch.add_plot(p + geom_density(kernel='epanechikov', bw=bw, n=n) + \
                           ggtitle('bw={0}, n={1}'.format(bw, n)),
                       j * 300, i * 200, 300, 200)
bunch.show()

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
np.random.seed(42)
x = np.random.normal(size=1000)
y = np.sign(x)
p = ggplot({'x': x, 'y': y}, aes(x='x'))
bunch = GGBunch()
for i, adjust in [(i, .5 * (1 + i)) for i in range(3)]:
    bunch.add_plot(p + geom_density(aes(weight='y'), kernel='cosine', \
                                    adjust=adjust) + \
                       ggtitle('adjust={0}'.format(adjust)),
                   i * 300, 0, 300, 200)
bunch.show()